Missing-not-at-random selection bias is also frequently discussed in recommender systems, where we can only observe feedback of displayed user-item pairs [1]}[2]}.
In counterfactual recommendation, propensity score is also applied, and the Inverse Propensity Score (IPS) approach [3]}[4]} that re-weights observed samples with the inverse of displayed probabilities is proposed.
Based on IPS, Doubly Robust (DR) [5]} and Joint Learning Doubly Robust (DRJL) [6]} are proposed to consider doubly robust estimator.
After that, some improvements have been presented, such as asymmetrically tri-training [7]}, considering information theory [8]}, and proposing better doubly robust estimators [9]}.
Meanwhile, the Adversarial Counterfactual Learning (ACL) approach [10]} incorporates adversarial learning for counterfactual recommendation.
Besides, some works on counterfactual recommendation rely on a small amount of random unbiased data [11]}[12]}[13]}.
However, random data requires high costs, especially in financial applications.
